Real-time health assessment for hydraulic pumps is a desired function due to, among other things, the high cost of unexpected failure of hydraulic systems. Typical hydraulic systems include both hydraulic-mechanical and electronic components, but most faults occur in the hydraulic-mechanical components. Common hydraulic system faults include, but are not limited to distortion, stress rupture, erosion, rubbing abrasion, impacting rupture, heat stress, and hot distortion. Furthermore, a hydraulic transmission and control system has its own special faults, such as oil pollution, leakage, air erosion, hydraulic blocking, pipe resonance, distortion of electrical signal, noise, and system surging.
Many existing fault diagnosis methods for hydraulic systems are based on mechanical system parameters, with feature signals such as vibration, acoustic noise, and temperatures. However, because these parameters are indirect measures of hydraulic system operating conditions, and due to the multiple motion forms of hydraulic-mechanical components and the interference of multiple components via the hydraulic lines, a more complicated process is required to use these indirect parameters to monitor a state of the hydraulic system sensitively and accurately.
For example, operation status of a hydraulic pump, a core component in a hydraulic system, directly influences the reliability of the hydraulic system. In piston-type hydraulic pumps, for example, common faults include, but are not limited to, worn pistons, swash plates, and distributing discs, bearing and spring failures, and loose piston shoes. These faults are often reflected in the pump discharge pressure, but are normally buried in the pulsation pressure signals. In addition, there are other noise sources, such as air erosion, hydraulic blocking, pipe resonance and leakage, etc. reflected in the pump discharge pressure. These noises normally result in a very low signal-to-noise ratio in the pump discharge pressure signals. Conventional health diagnosis methods, such as limit checking, spectrum analysis, and logic reasoning, require a distinguishable feature signal to detect faults, but these methods heretofore have not been sensitive or robust enough to reliably detect pump faults.
To obtain more reliable pump health diagnosis results, vibration analysis methods based on spectral analysis have been disclosed. In an exemplary vibration-based diagnosis method, an accelerometer is installed on the shell of the pump, and fault diagnosis is performed by spectral analysis of the shell vibration signals. Diagnosis methods may include, for example: (1) calculating an over-limit mean square amplitude of the vibration, in which a fault state is diagnosed in the mean square value exceeds a preset threshold; (2) characteristic frequency analysis, in which the frequency spectrum of obtained vibration signals is compared with that of a normal vibration signal, where the fault signal and/or pattern is identified based on differences between the evaluating spectrum and the normal spectrum; and (3) time-frequency domain analysis, in which feature patterns are extracted based on signal distributions on both time and frequency domain signals to diagnose faults of the system.